Measures to enforce mandatory civil building energy efficiency codes in China

Mandatory civil building energy efficiency codes strictly govern the energy consumption of new buildings in China. As the promotion of building energy efficiency in China has increased in recent years, compliance with mandatory civil building energy efficiency codes has also improved, increasing from less than 10% in 2000 to nearly 100% in 2012, a remarkable achievement. However, because the promotion of energy efficiency strategies in China has followed a unique pattern, some researchers doubt these statistics. In response to these doubts, this paper summarises and analyses the framework of measures implemented by the Chinese government to enforce mandatory building energy efficiency codes. First, the development and implementation of China's mandatory civil building energy efficiency code system is summarised. Second, the building supervision and inspection systems used to assess energy efficiency are introduced and analysed in detail in order to provide a framework for the development of energy policies in other countries. Third, the assessment and reporting processes used to determine compliance rates are reviewed. Finally, the improvement of compliance rates and its impact on building energy savings in China are discussed. Along with the increase in compliance rates in the construction stage from 71% in 2007 to 100% in 2012, the energy savings of new buildings per increased floor area per year increased from 20.4 kWh/m2 to 28.4 kWh/m2. The supervision and inspection systems reported in this paper are the keys to enforcing building energy efficiency codes

A subject-specific EMG-driven musculoskeletal model for applications in lower-limb rehabilitation robotics

Robotic devices have great potential in physical therapy owing to their repeatability, reliability and cost economy. However, there are great challenges to realize active control strategy, since the operator’s motion intention is uneasy to be recognized by robotics online. The purpose of this paper is to propose a subject-specific electromyography (EMG)-driven musculoskeletal model to estimate subject’s joint torque in real time, which can be used to detect his/her motion intention by forward dynamics, and then to explore its potential applications in rehabilitation robotics control. The musculoskeletal model uses muscle activation dynamics to extract muscle activation from raw EMG signals, a Hill-type muscle-tendon model to calculate muscle contraction force, and a proposed subject-specific musculoskeletal geometry model to calculate muscular moment arm. The parameters of muscle activation dynamics and muscle-tendon model are identified by off-line optimization methods in order to minimize the differences between the estimated muscular torques and the reference torques. Validation experiments were conducted on six healthy subjects to evaluate the proposed model. Experimental results demonstrated the model’s ability to predict knee joint torque with the coefficient of determination (R2) value of 0.934±0.0130.934±0.013 and the normalized root-mean-square error (RMSE) of 11.58%±1.44%11.58%±1.44%

Path Control of a Rehabilitation Robot Using Virtual Tunnel and Adaptive Impedance Controller

Interactive control strategies have been widely used in many rehabilitation robotic devices. The distinctive feature of these strategies is that the patient can be encouraged to actively participant in the therapy program. In this paper, a novel adaptive impedance control method, which allows the patient to actively influence the robot movement trajectory, is presented. The control algorithm developed in this paper is capable of regulating the desired impedance according to the patient's actual deviation from the desired path and the dynamic relationship between patients' motion intention and the reference trajectory. A virtual tunnel surrounding the reference trajectory is designed to ensure the patient's range of motion is always physiologically meaningful. The proposed rehabilitation strategy encourages participants to make contributions to rehabilitation training task as much as possible, which may facilitate provoking motor plasticity and motor recovery. Preliminary experiments with several healthy subjects were conducted to evaluate the feasibility and effectiveness of this strategy. Experimental results demonstrated that subjects could successfully finish the tracking task assisted by robot with the proposed control algorithm

An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation

This paper proposes an electromyography (EMG)-based method for online force prediction and control of a lower limb rehabilitation robot. Root mean square (RMS) features of EMG signals from four muscles of the lower limb are used as the inputs to a support vector regression (SVR) model to estimate the human-robot interaction force. The autoregressive algorithm is utilized to construct the relationship between EMG signals and the impact force. Combining the force prediction model with the position-based impedance controller, the robot can be controlled to track the desired force of the lower limb, and so as to achieve an adaptive and active rehabilitation mode, which is adaptable to the individual muscle strength and movement ability. Finally, the method was validated through experiments on a healthy subject. The results show that the EMG-based SVR model can predict the lower limb force accurately and the robot can be controlled to track the estimated force by using simplified impedance model

An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation

This paper proposes an electromyography (EMG)-based method for online force prediction and control of a lower limb rehabilitation robot. Root mean square (RMS) features of EMG signals from four muscles of the lower limb are used as the inputs to a support vector regression (SVR) model to estimate the human-robot interaction force. The autoregressive algorithm is utilized to construct the relationship between EMG signals and the impact force. Combining the force prediction model with the position-based impedance controller, the robot can be controlled to track the desired force of the lower limb, and so as to achieve an adaptive and active rehabilitation mode, which is adaptable to the individual muscle strength and movement ability. Finally, the method was validated through experiments on a healthy subject. The results show that the EMG-based SVR model can predict the lower limb force accurately and the robot can be controlled to track the estimated force by using simplified impedance model